Browsing by Author "Rashmi M."

Now showing 1 - 3 of 3

Algorithmic aspects of k-part degree restricted domination in graphs
(2020) Kamath S.S.; Senthil Thilak A.; Rashmi M.
The concept of network is predominantly used in several applications of computer communication networks. It is also a fact that the dominating set acts as a virtual backbone in a communication network. These networks are vulnerable to breakdown due to various causes, including traffic congestion. In such an environment, it is necessary to regulate the traffic so that these vulnerabilities could be reasonably controlled. Motivated by this, k-part degree restricted domination is defined as follows. For a positive integer k, a dominating set D of a graph G is said to be a k-part degree restricted dominating set (k-DRD set) if for all u ∈ D, there exists a set Cu ⊆ N(u) ∩(V − D) such that |Cu| ≤ ⌈d(ku) ⌉ and Su∈D Cu = V − D. The minimum cardinality of a k-DRD set of a graph G is called the k-part degree restricted domination number of G and is denoted by γ dk (G). In this paper, we present a polynomial time reduction that proves the NP-completeness of the k-part degree restricted domination problem for bipartite graphs, chordal graphs, undirected path graphs, chordal bipartite graphs, circle graphs, planar graphs and split graphs. We propose a polynomial time algorithm to compute a minimum k-DRD set of a tree and minimal k-DRD set of a graph. © 2020 World Scientific Publishing Co. Pte Ltd. All rights reserved.
Skeleton based Human Action Recognition for Smart City Application using Deep Learning
(2020) Rashmi M.; Guddeti R.M.R.
These days the Human Action Recognition (HAR) is playing a vital role in several applications such as surveillance systems, gaming, robotics, and so on. Interpreting the actions performed by a person from the video is one of the essential tasks of intelligent surveillance systems in the smart city, smart building, etc. Human action can be recognized either by using models such as depth, skeleton, or combinations of these models. In this paper, we propose the human action recognition system based on the 3D skeleton model. Since the role of different joints varies while performing the action, in the proposed work, we use the most informative distance and the angle between joints in the skeleton model as a feature set. Further, we propose a deep learning framework for human action recognition based on these features. We performed experiments using MSRAction3D, a publicly available dataset for 3D HAR, and the results demonstrated that the proposed framework obtained the accuracies of 95.83%, 92.9%, and 98.63% on three subsets of the dataset AS1, AS2, and AS3, respectively, using the protocols of [19]. © 2020 IEEE.
Surveillance video analysis for student action recognition and localization inside computer laboratories of a smart campus
(2021) Rashmi M.; Ashwin T.S.; Guddeti R.M.R.
In the era of smart campus, unobtrusive methods for students’ monitoring is a challenging task. The monitoring system must have the ability to recognize and detect the actions performed by the students. Recently many deep neural network based approaches have been proposed to automate Human Action Recognition (HAR) in different domains, but these are not explored in learning environments. HAR can be used in classrooms, laboratories, and libraries to make the teaching-learning process more effective. To make the learning process more effective in computer laboratories, in this study, we proposed a system for recognition and localization of student actions from still images extracted from (Closed Circuit Television) CCTV videos. The proposed method uses (You Only Look Once) YOLOv3, state-of-the-art real-time object detection technology, for localization, recognition of students’ actions. Further, the image template matching method is used to decrease the number of image frames and thus processing the video quickly. As actions performed by the humans are domain specific and since no standard dataset is available for students’ action recognition in smart computer laboratories, thus we created the STUDENT ACTION dataset using the image frames obtained from the CCTV cameras placed in the computer laboratory of a university campus. The proposed method recognizes various actions performed by students in different locations within an image frame. It shows excellent performance in identifying the actions with more samples compared to actions with fewer samples. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.